PROJECT SUMMARY One of the strongest genetic findings in schizophrenia is an excess of structural variation (SV) in the form of rare deletions and duplications in cases compared to controls. While a few genes and gene sets have been implicated by SV there does not currently exist a specific biological hypothesis underlying this finding. Further, only a small proportion of all SV has been studied due to the limited resolution of genotyping arrays (the most commonly used technology) to detect shorter deletions and duplications and more complex SV such as inversions. Efforts to understand a functional role of SV through measures of transcriptional activity have focused only on a few large, recurrent events, yet a large proportion of this excess is comprised of SV distributed across the genome. Further, most published work has looked at expression changes in blood as opposed to directly assessing the functional consequences of these events in the brain, which is the most relevant tissue for SCZ. This proposal will leverage existing NIMH funded datasets of 1,018 whole genome sequenced and transcriptionally profiled human post-mortem brains, as well as 39 exome-sequenced and transcriptionally profiled human induced pluripotent stem cell (hiPSC) derived neural progenitor cells and neurons of SCZ cases and controls to help interpret the functional role of SV in the brains of SCZ patients. Specifically, Aim 1 will apply the latest methods to discover SV from genotyping arrays, exome- sequencing data and whole genome sequencing data to build a high quality and comprehensive set of SV across all 1,057 brain samples. Aim 2 will look to quantify the effect SV has on expression in the brain and to what degree features such as size, frequency, proportion of gene hit and type of SV play a role. We anticipate these data will help infer transcriptional consequences of SV in samples without transcriptome data. Aim 3 is focused on how SV and SV driven transcriptional activity relate to SCZ risk. This aim includes assessing the contribution of a comprehensive set of SV to SCZ risk. Finally, this aim will use gene-gene co-expression and previously implicated genes in SCZ to identify transcriptional signatures of SV that increase risk of SCZ. At the conclusion of this grant we will have compiled a comprehensive survey of the mutational spectrum of SV in a large SCZ dataset of post-mortem brains. We will have provided a detailed examination of how SV alters transcription in the human brain and in hiPSC derived neural cells including a comparison between the two that will help assess the use of these cells for further genetic studies of transcription. Finally, we will have assessed the contribution of SV to SCZ risk and explored the existence of a functional signature that maps onto increased SV in SCZ cases compared to controls. The data and results created by this grant will be broadly useful in interpreting the role SV on expression in other transcriptional studies particularly in the brain. In addition, successful identification of a disease relevant transcriptional signature could be followed up further in hiPSC derived neural cells that can be manipulated and tested for cellular phenotypic consequences.